class_docstr: A BaseChannel that refers to the resolver function's output.
  
  For example:
  
      trainer_inputs = trainer_resolver_function(
          examples=example_gen.outputs['examples'])
  
      trainer = Trainer(examples=trainer_inputs['examples'])
  
  then the `trainer_inputs['examples']` is a ResolvedChannel instance.
class_docstr_tok: ["A","BaseChannel","that","refers","to","the","resolver","function","'s","output",".","For","example",":","trainer_inputs","=","trainer_resolver_function","(","examples=example_gen.outputs","[","'examples","'","]",")","trainer","=","Trainer","(","examples=trainer_inputs","[","'examples","'","]",")","then","the","`","trainer_inputs","[","'examples","'","]","`","is","a","ResolvedChannel","instance","."]
code: def invocation(self) -> Invocation:
      return self._invocation
  
file_path: tfx/tfx/types/resolved_channel.py
imports: import dataclasses
  from typing import Any, Optional, Type, Set, Sequence, Mapping
  from tfx.dsl.control_flow import for_each_internal
  from tfx.dsl.input_resolution import resolver_op
  from tfx.types import artifact
  from tfx.types import channel
  from tfx.utils import doc_controls
  from tfx.utils import typing_utils
label_desc: Use image node_id 6 for calling the ResolvedChannel obj's underlying member method code with example usage: obj.invocation() and returns: self